Seismic control of smart base isolated buildings with new semiactive variable damper
Bibliographic record
Abstract
Abstract A new semiactive independently variable damper, SAIVD, is developed and shown to be effective in achieving response reductions in smart base isolated buildings in near fault earthquakes. The semiactive device consists of four linear visco‐elastic elements, commonly known as Kelvin–Voigt elements, arranged in a rhombus configuration. The magnitude of force in the semiactive device can be adjusted smoothly in real‐time by varying the angle of the visco‐elastic elements of the device or the aspect ratio of the rhombus configuration. Such a device is essentially linear, simple to construct, and does not present the difficulties commonly associated with modelling and analysing nonlinear devices (e.g. friction devices). The smooth semiactive force variation eliminates the disadvantages associated with rapid switching devices. Experimental results are presented to verify the proposed analytical model of the device. A H∞ control algorithm is implemented in order to reduce the response of base isolated buildings with variable damping semiactive control systems in near fault earthquakes. The central idea of the control algorithm is to design a H∞ controller for the structural system that serves as an aid in the determination of the optimum control force in the semiactive device. The relative performance of the SAIVD device is compared to a variable friction device, recently developed by the authors in a separate study, and several key aspects of performance are discussed regarding the use of the two devices for reducing the responses of smart base isolated buildings in near fault earthquakes. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".